Machine Learning-Optimized Energy Management for Resilient Residential Microgrids with Dynamic Electric Vehicle Integration

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A machine learning-enhanced energy management framework can dynamically integrate solar, wind, battery storage, and electric vehicles to optimize the resilience and operation of residential microgrids. Novelty: ClaimNovelty.METHODOLOGICAL. Consensus alignment: ConsensusAlignment.NEUTRAL.

Mohammed M. Alenazi

doi:10.32604/jai.2025.066067

January 1, 2025Journal on artificial intelligenceOpen Access

Machine Learning-Optimized Energy Management for Resilient Residential Microgrids with Dynamic Electric Vehicle Integration

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Key Points

To develop an enhanced energy management framework for residential microgrids using machine learning techniques.
Utilized machine learning algorithms for optimizing energy distribution.
Integrated solar photovoltaics and wind turbines into the microgrid setup.
Implemented lithium-ion battery storage for efficient energy use.
Achieved significant improvements in energy efficiency compared to traditional systems.
Enhanced resilience of microgrids under varying energy demands and supply conditions.
Facilitated seamless integration of dynamic electric vehicle charging loads.

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Implication

Demonstrates optimized energy management in residential microgrids, indicating improved resilience.

Abstract

This paper presents a novel machine learning (ML) enhanced energy management framework for residential microgrids. It dynamically integrates solar photovoltaics (PV), wind turbines, lithium-ion battery energy storage systems ... | Find, read and cite all the research you need on Tech Science Press

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Mohammed M. Alenazi (2025) studied this question.

synapsesocial.com/papers/6a23bf94f11d41932608e44d https://doi.org/10.32604/jai.2025.066067

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Machine Learning-Optimized Energy Management for Resilient Residential Microgrids with Dynamic Electric Vehicle Integration

Key Points

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Abstract

Discussion

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Machine Learning-Optimized Energy Management for Resilient Residential Microgrids with Dynamic Electric Vehicle Integration

Key Points

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Implication

Abstract

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